The latest articles on DEV Community by Maria Siewierska (@maria-dac). https://dev.to/maria-dac https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3820336%2F109a55f4-2ed7-4b41-ad9e-68813dc685c0.jpg https://dev.to/maria-dac en Maria Siewierska Thu, 12 Mar 2026 12:14:25 +0000 https://dev.to/maria-dac/a-hands-on-ai-agents-tutorial-using-agno-openai-and-phoenix-4j74 https://dev.to/maria-dac/a-hands-on-ai-agents-tutorial-using-agno-openai-and-phoenix-4j74 <p>At DAC.digital, we wanted to cut through the hype and explain how AI agents actually work under the hood. So our CTO, Krzysztof Radecki, recorded a practical walkthrough that demonstrates the mechanics of agentic AI step by step. This post explains how we designed that tutorial and the technologies used to build it.</p> <h2> The Goal Was to Show the Tech </h2> <p>A lot of AI content focuses on outcomes, but there's not enough content that explains how the system works.</p> <p>Our goal for the tutorial was to demonstrate three key things: </p> <ol> <li><p>What a large language model really is</p></li> <li><p>How tokens and context windows shape model behavior </p></li> <li><p>How AI agents are built by orchestrating systems around an LLM</p></li> </ol> <p>To make this tangible, we created a small agent-based application and progressively extended it during the demo.</p> <p>As said before, there’s a lot of hype around AI agents, but building reliable systems requires understanding the fundamentals. An AI agent is not just an LLM. It’s a system architecture that combines: a language model context management memory tools guardrails orchestration logic The tutorial was designed to make that architecture visible.</p> <h2> Technology Stack </h2> <p>To build the tutorial environment, we combined several tools commonly used in modern AI systems like Agno (Agent Framework), OpenAI tokenizer tool, Arize Phoenix.</p> <p>The core of the demo is built using Agno, a framework designed for building AI agents. Agno makes it easier to orchestrate: LLM calls conversation history session management tool execution In the tutorial we use Agno to create several progressively more capable agents: a basic stateless agent an agent with session history an agent with persistent memory This allows viewers to see exactly how each capability changes the model’s behavior.</p> <p>In the demo, the LLM behaves exactly as a raw model would and each prompt is independent, there's no memory that is stored between calls, and the model simply predicts the next tokens.</p> <p>Watch the Full Walkthrough</p> <p>If you'd like to see the system in action, you can <a href="https://www.youtube.com/watch?v=qHmqExKVO5w" rel="noopener noreferrer">watch the full tutorial here</a>.</p> agents ai automation